Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform LiDAR echoes. We present a new approach to deal with the ”Pseudo-monopulse” waveform formed by the overlapped waveforms from multi-targets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multi-targets. The geometric center position distribution of the ”Pseudo-monopulse” presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulse width, and skew-coefficient) between the echo and fitted SND waveform and 2) conducting single-channel waveform decomposition for all channels and 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position) and 4) re-conducting single-channel waveform decomposition for these outlier channels. The proposed method significantly improves the range resolution from 60cm to 5cm at most for a 4ns width laser pulse and represents the state-of-the-art in ”Pseudo-monopulse” waveform decomposition.

In cloud service systems, customers will report the service issues they have encountered to cloud service providers. Despite many issues can be handled by the support team, sometimes the customer issues can not be easily solved, thus raising customer incidents. Quick troubleshooting of a customer incident is critical. To this end, a customer incident should be assigned to its responsible team accurately in a timely manner.

The security issue of public WiFi is gaining more and more concern. By listening to probe requests, an adversary can obtain the SSID list of the APs to which a smartphone previously connected, and utilizes this information to trick the smartphone into associating to it. However, with the enhancement of security level, most smartphones now do not proactively disclose their SSID lists, making these attacks obsolete. In this paper, we propose City-Hunter, an attacker that can lure nearby smartphones without knowing their SSID information. City-Hunter establishes and maintains an SSID database by integrating both offline and online information. Meanwhile, it smartly chooses some SSIDs to hit a smartphone according to the past record and freshness. We evaluate the performance of City-Hunter in different public places. The results demonstrate that City-Hunter is able to successfully hit 12% ~ 18% smartphones without knowing their SSID information, which is about 4 ~ 8 times improvement compared to the similar attacks like KARMA and MANA.

Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver's mobile phone from passengers'. Existing solutions to this problem generally rely on the user's manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver's location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructurefree and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.

It is well recognized that walking while using mobile phones will make people more susceptible at various risks. Existing works to improve smartphone users' safety are mainly limited to detecting incoming vehicles. They are not able to address some more common and equally dangerous accidents such as trips, falling from stairs, platforms, or falling into an open manhole. These hazards are generally caused by a sudden change of ground. In this paper, we propose InfraSee, a system that is able to detect sudden change of ground for pedestrian mobile phone users. InfraSee augments smartphones with a small infrared sensor which measures the distance of the ground surface from the sensor. The temporal variation of distance can provide information about the change of ground surface ahead. InfraSee also leverages the information of smartphone sensors to improve detection accuracy, to reduce energy consumption, and to avoid unnecessary alarms. We have carried out extensive experiments in different scenarios and by different users. The results show that InfraSee is able to reliably detect about 80 percent change of ground surfaces. In addition, InfraSee can reliably identify the awareness of smartphone users and reduce unnecessary alarms.

Non-invasive human sensing based on radio signals has attracted numerous research interests in recent years. Previous work mainly focused on detecting the presence of a person or identifying human gestures and activities. In this paper, we show that with off-the-shelf WiFi devices, fine-grained respiration information of a person under different sleeping positions can be extracted successfully. We do this by introducing a breath monitoring system based on WiFi signals. This system adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, the rhythmic patterns associated with respiration and abrupt changes due to the body movement are identified. Compared to existing respiration monitoring systems that usually require special devices attached to human body, this system is completely contactless. In addition, different from many vision-based sleep monitoring systems, it is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that our system can reliably track a person's respiration reliably in different sleeping postures.

Event-triggered wake-up, in which sensor nodes wake up to work in the presence of some pre-defined events, has been widely used in wireless sensor networks (WSNs) to save energy while still completing the tasks required. However, some recently emerged domain-specific WSN applications such as structural health monitoring (SHM) and volcano seismic tomography, have different requirements with regard to wake-up as compared to conventional WSN applications. In these domain-specific applications, the wake-up should be network-wide and nodes to be woken up are not limited to those close to event locations. In addition, the wake-up should be fast to capture enough information during generally short events and be reliable to avoid costly false-positive wake-ups. This problem has not been addressed in the literature. In this paper, we designed two types of wake-up units, based on which we propose a new chain-reaction wake-up mechanism to address this challenge. In this mechanism, we carefully select some nodes used to initiate the wake-up process, such that the wake-up delay is minimized under the false alarm constraint. We propose two greedy algorithms and a randomized one that leverages the solution to the classic Knapsack problem. The performance of the proposed wake-up mechanism is demonstrated through both simulation and experiments.

It is well recognized that walking while using mobile phones will make people more susceptible at various risks. Existing studies to improve smartphone users' safety are mainly limited to detecting incoming vehicles. They are not able to address some more common and equally dangerous accidents such as trips, falling from stairs, platforms or falling into an open manhole. These hazards are generally caused by sudden change of ground. In this paper, we propose UltraSee, the first system that is able to detect sudden change of ground for pedestrian mobile phone users. UltraSee augments smartphones with a small ultrasonic sensor which can detect the abrupt change of distance ahead. UltraSee also leverages the context information of smartphone usage such as screen status and holding orientation to improve detection accuracy and reduce energy consumption as well as unnecessary alarms. We have carried out extensive experiments in different scenarios and by different users. The results show that UltraSee can achieve accident detection rate of 94% with false positive rate of 4.4% and reduce unnecessary alarms by 90%. In terms of energy consumption, UltraSee costs only about 20% energy compared to the existing works that only rely on smartphone cameras.

Is it possible to leverage WiFi signals collected in bedrooms to monitor a person's sleep? In this paper, we show that with off-the-shelf WiFi devices, fine-grained sleep information like a person's respiration, sleeping postures and rollovers can be successfully extracted. We do this by introducing Wi-Sleep, the first sleep monitoring system based on WiFi signals. Wi-Sleep adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, Wi-Sleep extracts rhythmic patterns associated with respiration and abrupt changes due to the body movement. Compared to existing sleep monitoring systems that usually require special devices attached to human body (i.e. Probes, head belt, and wrist band), Wi-Sleep is completely contact less. In addition, different from many vision-based sleep monitoring systems, Wi-Sleep is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that the Wi-Sleep can reliably track a person's respiration and sleeping postures in different conditions.